code: def return_and_correct_aliasing(func, args, kwargs, out):
      """
      This function should be used by wrapper tensor ``__torch_dispatch__`` subclasses
      that would like to work with torch.compile. It ensures that the subclass
      properly implements the aliasing behavior of every op,
      which is needed for correctness in AOTAutograd.
      This function will handle:
  
          * When we see a view op, we will alias the storages of any
            input and output tensor subclasses
  
          * When we see an inplace or out= op, we will directly
            return the corresponding input tensor, instead of returning
            a (potentially) fresh output tensor.
      """
  
      # Caching here because torchgen parsing is definitely not fast, and this function is called
      # once for every op in the graph during functionalization.
      schema_info = get_alias_info(func)
  
      def get_write_alias(x):
          if len(x.alias_set) == 0:
              return None
          alias_set = list(x.alias_set)
          # torchscript allows for complicated alias sets, but our dispatcher ops only really involve simple aliasing
          assert len(alias_set) == 1
          if x.is_write:
              return alias_set[0]
          return None
  
      def get_arg_from_alias(output_alias, schema_info, args, kwargs):
          (
              new_args,
              new_kwargs,
          ) = torch.fx.operator_schemas.normalize_function(
              func, args=args, kwargs=kwargs
          )
  
          arg_indices = [
              i
              for i, a in enumerate(schema_info.args)
              if output_alias in a.alias_set
          ]
          # For any dispatcher op with an output alias, we expect it to map to exactly one alias in the schema's input arguments.
          assert len(arg_indices) == 1
          idx = arg_indices[0]
          arg_info = schema_info.args[idx]
          if arg_info.name is not None and arg_info.name in new_kwargs:
              return new_kwargs[arg_info.name]
          return new_args[idx]
  
      # Fix up the storages of any outs so that they point to the same storage as the input,
      # if func is a view op.
      _correct_storage_aliasing(
          func,
          schema_info,
          args,
          (out,) if not isinstance(out, tuple) else out,
      )
  
      # For inplace_view ops in particular, we'll try hard to make sure that the wrapper subclass's
      # metadata is set correctly.
      if torch.Tag.inplace_view in func.tags:
          # no_dispatch() to make sure that we secretly change the metadata on the wrapper,
          # but don't end up dispatching the op anywhere else.
          mutated_args = [
              x
              for i, x in enumerate(args)
              if get_write_alias(schema_info.args[i]) is not None
          ]
          # Assumption: we have a very small number of inplace_view ops that follow a strict schema:
          # there is only a single argument that gets its metadata mutated.
          assert len(mutated_args) == 1
          # This check exists because we generally *do* want to update the metadata of any wrapper subclasses,
          # but FunctionalTensor is special: it overrides all size/stride calls to plumb to the inner tensor.
          # so we don't actually need to update the metadata (and attempting to do so causes errors)
          from torch._subclasses.functional_tensor import (
              FunctionalTensor,
          )
  
          if not isinstance(mutated_args[0], FunctionalTensor):
              with torch.utils._mode_utils.no_dispatch():
                  # See Note: [Fake Tensor Dispatch Keys]
                  # we're borrowing the way it modifies dispatch key TLS.
                  meta_in_tls = torch._C._meta_in_tls_dispatch_include()
                  torch._C._set_meta_in_tls_dispatch_include(True)
                  try:
                      func(*args, **kwargs)
                  finally:
                      torch._C._set_meta_in_tls_dispatch_include(
                          meta_in_tls
                      )
  
      # Next: we need to make sure to return inputs directly, if the output is a mutable alias (e.g. add_()).
  
      # simple case: none of our outputs have mutable aliases, so we can return the output as-is
      if not any(
          get_write_alias(r) is not None for r in schema_info.outs
      ):
          return out
  
      # simplifying assumption: we don't have **any** ops with return types like "-> (Tensor(a!), Tensor)"
      if not all(
          get_write_alias(r) is not None for r in schema_info.outs
      ):
          raise RuntimeError("Unsupported schema: " + str(func._schema))
  
      if len(func._schema.returns) == 1:
          return get_arg_from_alias(
              get_write_alias(schema_info.outs[0]),
              schema_info,
              args,
              kwargs,
          )
  
      # In the multi-return case, all aten ops return a tuple / list, so cast accordingly.
      outs_to_return = type(out)(
          [
              get_arg_from_alias(
                  get_write_alias(schema_info.outs[i]),
                  schema_info,
                  args,
                  kwargs,
              )
              if get_write_alias(r) is not None
              else o
              for ((i, r), o) in zip(enumerate(schema_info.outs), out)
          ]
      )
      return outs_to_return
  
imports: import contextlib
  from typing import Optional, Union, List, Set, Dict, Any
  import warnings
  from dataclasses import dataclass
  import torch
  import torchgen
  from torch._C import _len_torch_dispatch_stack, _get_dispatch_stack_at, _pop_torch_dispatch_stack, _push_on_torch_dispatch_stack, DispatchKey
label_desc: Use image node_id 12 for calling a global function with example usage: return_and_correct_aliasing(func, args, kwargs, out) and returns: outs_to_return, None, new_args, out, get_arg_from_alias, None, alias_set, new_kwargs